Ensemble Model Detection of COVID-19 from Chest X-Ray Images

The rapid advancement of deep learning techniques usage in treating several medical issues and search for innovative methods in predicting COVID-19 is the leading cause for this finding. Feature-level ensemble model is proposed to distinguish COVID-19 cases from other similar lung infections. Three different pre-trained CNN models, namely VGG16, DenseNet201, and EfficientNetB7 are tested and finally combined to form the proposed ensemble model. Ensemble approach synergizes the features extracted by deep CNN models to deliver accurate predictions and further improve classification. This approach not only enhances the model performance but also reduces generalization error as compared to a single model. To show the efficacy of this proposed model, it has been compared with the existing pre-trained models and tested for 3-class, 4-class, and 5-class on public available datasets. The proposed models’ performance is estimated in terms of accuracy, precision, recall, and f1-score parameters and achieved better results for detection purpose. Hence, the proposed model is a promising diagnostic tool for accurate screening of COVID-19 disease.